Position Paper: Integrating Explainability and Uncertainty Estimation in Medical AI

Abstract

Uncertainty is a fundamental challenge in medical practice, but current medical AI systems fail to explicitly quantify or communicate uncertainty in a way that aligns with clinical reasoning. Existing XAI works focus on interpreting model predictions but do not capture the confidence or reliability of these predictions. Conversely, uncertainty estimation (UE) techniques provide confidence measures but lack intuitive explanations. The disconnect between these two areas limits AI adoption in medicine. To address this gap, we propose Explainable Uncertainty Estimation (XUE) that integrates explainability with uncertainty quantification to enhance trust and usability in medical AI. We systematically map medical uncertainty to AI uncertainty concepts and identify key challenges in implementing XUE. We outline technical directions for advancing XUE, including multimodal uncertainty quantification, model-agnostic visualization techniques, and uncertainty-aware decision support systems. Lastly, we propose guiding principles to ensure effective XUE realisation. Our analysis highlights the need for AI systems that not only generate reliable predictions but also articulate confidence levels in a clinically meaningful way. This work contributes to the development of trustworthy medical AI by bridging explainability and uncertainty, paving the way for AI systems that are aligned with real-world clinical complexities.