mlsanityData to 7 Oct 2026

Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

arXiv 1907.01949 ↗First posted 3 Jul 2019cs.LG cs.CV stat.MLcited by 42

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Abstract (arXiv)

The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertainty do not return calibrated uncertainty estimates, which could be used in practice. In this work we exploit multi-grader annotation variability as a source of 'groundtruth' aleatoric uncertainty, which can be treated as a target in a supervised learning problem. We combine this groundtruth uncertainty with a Probabilistic U-Net and test on the LIDC-IDRI lung nodule CT dataset and MICCAI2012 prostate MRI dataset. We find that we are able to improve predictive uncertainty estimates. We also find that we can improve sample accuracy and sample diversity. In real-world applications, our method could inform doctors about the confidence of the segmentation results.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jul 2019.

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Citing papers per year: 2021: 14; 2022: 9; 2023: 9; 2024: 6; 2025: 1; 2026: 3.

42 papers.

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  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  2. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  3. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  4. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  5. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  6. 2023Stochastic Segmentation with Conditional Categorical Diffusion Modelscited by 38
  7. 2024ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentationcited by 25
  8. 2022ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-trainingcited by 20
  9. 2021Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesiscited by 19
  10. 2021Using Soft Labels to Model Uncertainty in Medical Image Segmentationcited by 18
  11. 2021Robustness via Uncertainty-aware Cycle Consistencycited by 16
  12. 2022Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Expertscited by 13
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  1. 2021Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentationcited by 12
  2. 2022Generalized Probabilistic U-Net for medical image segementationcited by 10
  3. 2023Effect of latent space distribution on the segmentation of images with multiple annotationscited by 7
  4. 2021Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attentioncited by 6
  5. 2023Transmission-Guided Bayesian Generative Model for Smoke Segmentationcited by 6
  6. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  7. 2021Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detectioncited by 4
  8. 2021Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI -Should Different Clinical Objectives Mandate Different Loss Functions?cited by 4
  9. 2022Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structurescited by 4
  10. 2023Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical datacited by 4
  11. 2023Confidence Contours: Uncertainty-Aware Annotation for Medical Semantic Segmentationcited by 4
  12. 2021Uncertainty-aware Generalized Adaptive CycleGANcited by 3
  13. 2021A Decoupled Uncertainty Model for MRI Segmentation Quality Estimationcited by 3
  14. 2023Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanismcited by 3
  15. 2021Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Netcited by 2
  16. 2022Looking at the posterior: accuracy and uncertainty of neural-network predictionscited by 2
  17. 2023Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translationcited by 2
  18. 2021An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growthcited by 1
  19. 2021Medical Matting: A New Perspective on Medical Segmentation with Uncertaintycited by 1
  20. 2021Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflowscited by 1
  21. 2022Distributional Gaussian Processes Layers for Out-of-Distribution Detectioncited by 1
  22. 2024Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentationcited by 1
  23. 2026Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentationcited by 1
  24. 2022Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantificationcited by 0
  25. 2024Multi-scale gridded Gabor attention for cirrus segmentationcited by 0
  26. 2024Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantificationcited by 0
  27. 2024Osteoporosis Prediction from Hand X-ray Images Using Segmentation-for-Classification and Self-Supervised Learningcited by 0
  28. 2025UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomographycited by 0
  29. 2026Pareto-Guided Optimization for Uncertainty-Aware Medical Image Segmentationcited by 0
  30. 2026Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variabilitycited by 0