We address the selection and evaluation of uncertain segmentation methods in medical imaging and present two case studies: prostate segmentation, illustrating that for minimal annotator variation simple deterministic models can suffice, and lung lesion segmentation, highlighting the limitations of the Generalized Energy Distance (GED) in model selection. Our findings lead to guidelines for accurately choosing and developing uncertain segmentation models, that integrate aleatoric and epistemic components. These guidelines are designed to aid researchers and practitioners in better developing, selecting, and evaluating uncertain segmentation methods, thereby facilitating enhanced adoption and effective application of segmentation uncertainty in practice.

翻译：本文针对医学影像中不确定性分割方法的选择与评估问题展开研究，并呈现两个案例：前列腺分割案例表明，在标注者差异极小的情况下，简单的确定性模型即可满足需求；肺病灶分割案例则揭示了广义能量距离在模型选择中的局限性。我们的研究结果形成了关于准确选择和开发不确定性分割模型的指导原则，这些原则融合了偶然不确定性和认知不确定性成分。这些指南旨在帮助研究者和实践者更好地开发、选择与评估不确定性分割方法，从而促进分割不确定性在实际应用中的广泛采纳与有效实施。