[2604.01898] Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling

Computer Science > Machine Learning arXiv:2604.01898 (cs) [Submitted on 2 Apr 2026] Title:Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling Authors:Aleksei Khalin, Ekaterina Zaychenkova, Aleksandr Yugay, Andrey Goncharov, Sergey Korchagin, Alexey Zaytsev, Egor Ershov View a PDF of the paper titled Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling, by Aleksei Khalin and 6 other authors View PDF HTML (experimental) Abstract: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 varian...