Medical image segmentation, a critical task in medical image analysis, aims to precisely delineate regions of interest (ROIs) such as organs, lesions, and cells, and is crucial for applications including computer-aided diagnosis, surgical planning, radiation therapy, and pathological analysis. While fully supervised deep learning methods have demonstrated remarkable performance in this domain, their reliance on large-scale, pixel-level annotated datasets—a significant label scarcity challenge—severely hinders their widespread deployment in clinical settings. Addressing this limitation, this review focuses on non-fully supervised learning paradigms, systematically investigating the application of semi-supervised, weakly supervised, and unsupervised learning techniques for medical image segmentation. We delve into the theoretical foundations, core advantages, typical application scenarios, and representative algorithmic implementations associated with each paradigm. Furthermore, this paper compiles and critically reviews commonly utilized benchmark datasets within the field. Finally, we discuss future research directions and challenges, offering insights for advancing the field and reducing dependence on extensive annotation.

Medical image segmentation, a critical task in medical image analysis, aims to precisely delineate regions of interest (ROIs) such as organs, lesions, and cells, and is crucial for applications including computer-aided diagnosis, surgical planning, radiation therapy, and pathological analysis. While fully supervised deep learning methods have demonstrated remarkable performance in this domain, their reliance on large-scale, pixel-level annotated datasets—a significant label scarcity challenge—severely hinders their widespread deployment in clinical settings. Addressing this limitation, this review focuses on non-fully supervised learning paradigms, systematically investigating the application of semi-supervised, weakly supervised, and unsupervised learning techniques for medical image segmentation. We delve into the theoretical foundations, core advantages, typical application scenarios, and representative algorithmic implementations associated with each paradigm. Furthermore, this paper compiles and critically reviews commonly utilized benchmark datasets within the field. Finally, we discuss future research directions and challenges, offering insights for advancing the field and reducing dependence on extensive annotations.