Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem. While different probabilistic segmentation models are designed to capture different aspects of segmentation uncertainty and ambiguity, these modelling differences are rarely discussed in the context of applications of uncertainty. We consider two common use cases of segmentation uncertainty, namely assessment of segmentation quality and active learning. We consider four established strategies for probabilistic segmentation, discuss their modelling capabilities, and investigate their performance in these two tasks. We find that for all models and both tasks, returned uncertainty correlates positively with segmentation error, but does not prove to be useful for active learning.

翻译：概率图像分解将不同的预测信心和分解问题的内在模糊性编码成不同的预测信任度和分解问题的内在模糊性。虽然不同的概率分解模型旨在捕捉分解不确定性和模糊性的不同方面,但这些建模差异很少在不确定性应用的范围内加以讨论。我们考虑了两种共同使用的分解不确定性案例,即分解质量评估和积极学习。我们考虑了四种既定的概率分解战略,讨论了其建模能力,并调查了它们在这两项任务中的绩效。我们发现,对所有模型和两个任务来说,返回的不确定性都与分解错误有积极的联系,但证明对积极学习没有用处。