# Spatially-Aware Evaluation of Segmentation Uncertainty

arXiv: https://arxiv.org/abs/2506.16589 · HTML: https://mlsanity.com/paper/2506.16589/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Evaluating Bayesian Deep Learning Methods for Semantic Segmentation](https://mlsanity.com/paper/1811.12709/) (2018)
- [Improving model calibration with accuracy versus uncertainty optimization](https://mlsanity.com/paper/2012.07923/) (2020)
- [Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers](https://mlsanity.com/paper/1805.08206/) (2018)
- [DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks](https://mlsanity.com/paper/1906.04569/) (2019)
- [Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation](https://mlsanity.com/paper/2412.07169/) (2024)

## Built on by (most cited first, 6 of 6)

- [SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation](https://mlsanity.com/paper/2511.10370/) (2025)
- [Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation](https://mlsanity.com/paper/2605.18329/) (2026)
- [Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation](https://mlsanity.com/paper/2508.17768/) (2025)
- [Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction](https://mlsanity.com/paper/2605.03148/) (2026)
- [Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation](https://mlsanity.com/paper/2606.19300/) (2026)
- [Rethinking Post-Hoc Calibration in Semantic Segmentation](https://mlsanity.com/paper/2607.01902/) (2026)

## Abstract (arXiv)

Uncertainty maps highlight unreliable regions in segmentation predictions. However, most uncertainty evaluation metrics treat voxels independently, ignoring spatial context and anatomical structure. As a result, they may assign identical scores to qualitatively distinct patterns (e.g., scattered vs. boundary-aligned uncertainty). We propose three spatially aware metrics that incorporate structural and boundary information and conduct a thorough validation on medical imaging data from the prostate zonal segmentation challenge within the Medical Segmentation Decathlon. Our results demonstrate improved alignment with clinically important factors and better discrimination between meaningful and spurious uncertainty patterns.