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Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling

arXiv 2604.01898 ↗First posted 2 Apr 2026cs.LGcited by 0

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Abstract (arXiv)

Artificial intelligence (AI) systems accelerate medical workflows and improve diagnostic accuracy in healthcare, serving as second-opinion systems. However, the unpredictability of AI errors poses a significant challenge, particularly in healthcare contexts, where mistakes can have severe consequences. A widely adopted safeguard is to pair predictions with uncertainty estimation, enabling human experts to focus on high-risk cases while streamlining routine verification. Current uncertainty estimation methods, however, remain limited, particularly in quantifying aleatoric uncertainty, which arises from data ambiguity and noise. To address this, we propose a novel approach that leverages disagreement in expert responses to generate targets for training machine learning models. These targets are used in conjunction with standard data labels to estimate two components of uncertainty separately, as given by the law of total variance, via a two-ensemble approach, as well as its lightweight variant. We validate our method on binary image classification, binary and multi-class image segmentation, and multiple-choice question answering. Our experiments demonstrate that incorporating expert knowledge can enhance uncertainty estimation quality by 9% to 50% depending on the task, making this source of information invaluable for the construction of risk-aware AI systems in healthcare applications.

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References · 25

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13 more
  1. 2022Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?cited by 125
  2. 2021Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibrationcited by 48
  3. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  4. 2021Estimating Expected Calibration Errorscited by 24
  5. 2022Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networkscited by 24
  6. 2024Label-wise Aleatoric and Epistemic Uncertainty Quantificationcited by 22
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  12. 2025A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlookcited by 7
  13. 2019KINN: Incorporating Expert Knowledge in Neural Networkscited by 4

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