Trustworthy artificial intelligence (AI) is essential in healthcare, particularly for high-stakes tasks like medical image segmentation. Explainable AI and uncertainty quantification significantly enhance AI reliability by addressing key attributes such as robustness, usability, and explainability. Despite extensive technical advances in uncertainty quantification for medical imaging, understanding the clinical informativeness and interpretability of uncertainty remains limited. This study presents an interpretability framework for analyzing lesion-scale predictive uncertainty in cortical lesion segmentation in multiple sclerosis using deep ensembles. The analysis shifts the focus from the uncertainty--error relationship towards clinically relevant medical and engineering factors. Our findings reveal that instance-wise uncertainty is strongly related to lesion size, shape, and cortical involvement. Expert rater feedback confirms that similar factors impede annotator confidence. Evaluations conducted on two datasets (206 patients, almost 2000 lesions) under both in-domain and distribution-shift conditions highlight the utility of the framework in different scenarios.

翻译：可信人工智能在医疗领域至关重要，尤其对于医学图像分割等高风险任务。可解释人工智能与不确定性量化通过提升鲁棒性、可用性和可解释性等关键属性，显著增强了人工智能的可靠性。尽管医学影像不确定性量化技术已取得广泛进展，但对其临床信息价值及可解释性的理解仍十分有限。本研究提出一个可解释性框架，利用深度集成方法分析多发性硬化皮层病变分割中病灶尺度的预测不确定性。该分析将关注点从“不确定性-误差”关系转向临床相关的医学与工程因素。研究发现，逐实例不确定性与病灶大小、形态及皮质受累程度密切相关。专家评分员的反馈证实，类似因素也阻碍了标注者的置信度。在两个数据集（206名患者，近2000个病灶）上进行的域内和分布偏移条件评估，突显了该框架在不同场景下的实用性。