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. Despite growing interest in probabilistic segmentation to address point-estimate limitations, the research landscape remains fragmented. In response, this review synthesizes foundational concepts in uncertainty modeling, analyzing how feature- and parameter-distribution modeling impact four key segmentation tasks: Observer Variability, Active Learning, Model Introspection, and Model Generalization. Our work establishes a common framework by standardizing theory, notation, and terminology, thereby bridging the gap between method developers, task specialists, and applied researchers. We then discuss critical challenges, including the nuanced distinction between uncertainty types, strong assumptions in spatial aggregation, the lack of standardized benchmarks, and pitfalls in current quantification methods. We identify promising avenues for future research, such as uncertainty-aware active learning, data-driven benchmarks, transformer-based models, and novel techniques to move from simple segmentation problems to uncertainty in holistic scene understanding. Based on our analysis, we offer practical guidelines for researchers on method selection, evaluation, reproducibility, and meaningful uncertainty estimation. Ultimately, our goal is to facilitate the development of more reliable, efficient, and interpretable segmentation models that can be confidently deployed in 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，忽略了稳健决策所需的关键不确定性信息。尽管概率分割方法因能解决点估计的局限性而受到越来越多的关注，但该研究领域仍处于碎片化状态。为此，本综述综合了不确定性建模的基本概念，分析了特征分布建模和参数分布建模如何影响四个关键分割任务：观察者变异性、主动学习、模型自省和模型泛化。通过标准化理论、符号和术语，我们的工作建立了一个通用框架，从而弥合了方法开发者、任务专家和应用研究人员之间的鸿沟。随后，我们讨论了关键挑战，包括不确定性类型间的细微区别、空间聚合中的强假设、标准化基准的缺乏以及当前量化方法中的缺陷。我们指出了未来研究的有前景方向，例如不确定性感知的主动学习、数据驱动的基准测试、基于Transformer的模型，以及从简单分割问题转向整体场景理解中不确定性建模的新技术。基于我们的分析，我们为研究人员提供了关于方法选择、评估、可重复性和有意义的不确定性估计的实用指南。最终，我们的目标是促进开发更可靠、高效和可解释的分割模型，使其能够被自信地部署在实际应用中。