Advances in architectural design, data availability, and compute have driven remarkable progress in semantic segmentation. Yet, these models often rely on relaxed Bayesian assumptions, omitting critical uncertainty information needed for robust decision-making. The resulting reliance on point estimates has fueled interest in probabilistic segmentation, but the literature remains fragmented. In response, this review consolidates and contextualizes foundational concepts in uncertainty modeling, including the non-trivial task of distinguishing between epistemic and aleatoric uncertainty and examining their roles across four key downstream segmentation tasks, highlighting Active Learning as particularly promising. By unifying theory, terminology, and applications, we provide a coherent foundation for researchers and identify critical challenges, such as strong assumptions in spatial aggregation, lack of standardized benchmarks, and pitfalls in current uncertainty quantification methods. We identify trends such as the adoption of contemporary generative models, driven by advances in the broader field of generative modeling, with segmentation-specific innovation primarily in the conditioning mechanisms. Moreover, we observe growing interest in distribution- and sampling-free approaches to uncertainty estimation. We further propose directions for advancing uncertainty-aware segmentation in deep learning, including pragmatic strategies for disentangling different sources of uncertainty, novel uncertainty modeling approaches and improved Transformer-based backbones. In this way, we aim to support the development of more reliable, efficient, and interpretable segmentation models that effectively incorporate uncertainty into real-world applications.

翻译：架构设计、数据可用性和计算能力的进步推动了语义分割领域的显著发展。然而，这些模型通常依赖于宽松的贝叶斯假设，忽略了鲁棒决策所需的关键不确定性信息。由此产生的对点估计的依赖激发了人们对概率分割的兴趣，但相关文献仍显零散。为此，本综述整合并梳理了不确定性建模的基础概念，包括区分认知不确定性和偶然不确定性这一非平凡任务，并考察了它们在四个关键下游分割任务中的作用，特别强调了主动学习作为极具前景的方向。通过统一理论、术语和应用，我们为研究人员提供了连贯的基础，并指出了关键挑战，例如空间聚合中的强假设、标准化基准的缺乏以及当前不确定性量化方法中的缺陷。我们识别出若干趋势，例如受更广泛生成建模领域进展的推动，当代生成模型正被采纳，而分割领域的创新主要体现在条件机制上。此外，我们观察到对无需分布和采样的不确定性估计方法的兴趣日益增长。我们进一步提出了推进深度学习不确定性感知分割的发展方向，包括解耦不同不确定性来源的实用策略、新颖的不确定性建模方法以及改进的基于Transformer的主干网络。通过这种方式，我们旨在支持开发更可靠、高效和可解释的分割模型，从而将不确定性有效地整合到实际应用中。