Advancements in image segmentation play an integral role within the broad scope of Deep Learning-based Computer Vision. Furthermore, their widespread applicability in critical real-world tasks has resulted in challenges related to the reliability of such algorithms. Hence, uncertainty quantification has been extensively studied within this context, enabling the expression of model ignorance (epistemic uncertainty) or data ambiguity (aleatoric uncertainty) to prevent uninformed decision-making. Due to the rapid adoption of Convolutional Neural Network (CNN)-based segmentation models in high-stake applications, a substantial body of research has been published on this very topic, causing its swift expansion into a distinct field. This work provides a comprehensive overview of probabilistic segmentation, by discussing fundamental concepts of uncertainty quantification, governing advancements in the field as well as the application to various tasks. Moreover, literature on both types of uncertainties trace back to four key applications: (1) to quantify statistical inconsistencies in the annotation process due ambiguous images, (2) correlating prediction error with uncertainty, (3) expanding the model hypothesis space for better generalization, and (4) Active Learning. An extensive discussion follows that includes an overview of utilized datasets for each of the applications and evaluation of the available methods. We also highlight challenges related to architectures, uncertainty quantification methods, standardization and benchmarking, and finally end with recommendations for future work such as methods based on single forward passes and models that appropriately leverage volumetric data.

翻译：图像分割技术的进步在基于深度学习的计算机视觉广阔领域中扮演着不可或缺的角色。此外，这些技术在关键现实任务中的广泛应用，也引发了关于此类算法可靠性的挑战。因此，不确定性量化在此背景下得到了广泛研究，它能够表达模型的认知不确定性或数据的随机不确定性，从而避免基于不充分信息的决策。由于基于卷积神经网络（CNN）的分割模型在高风险应用中的迅速普及，大量研究聚焦于此，使其迅速发展成为一个独立的领域。本文对概率分割进行了全面概述，讨论了不确定性量化的基本概念、该领域的主要进展以及在各种任务中的应用。此外，关于两类不确定性的文献可追溯至四个关键应用方向：（1）量化因图像模糊导致的标注过程中的统计不一致性；（2）将预测误差与不确定性相关联；（3）扩展模型假设空间以提升泛化能力；（4）主动学习。随后进行了广泛讨论，包括对各应用所用数据集的概述以及对现有方法的评估。我们还重点探讨了与架构、不确定性量化方法、标准化和基准测试相关的挑战，最后对未来研究方向提出建议，例如基于单次前向传播的方法以及能有效利用体数据（volumetric data）的模型。