  Medical image segmentation plays an important role in many image-guided
clinical approaches. However, existing segmentation algorithms mostly rely on
the availability of fully annotated images with pixel-wise annotations for
training, which can be both labor-intensive and expertise-demanding, especially
in the medical imaging domain where only experts can provide reliable and
accurate annotations. To alleviate this challenge, there has been a growing
focus on developing segmentation methods that can train deep models with weak
annotations, such as image-level, bounding boxes, scribbles, and points. The
emergence of vision foundation models, notably the Segment Anything Model
(SAM), has introduced innovative capabilities for segmentation tasks using weak
annotations for promptable segmentation enabled by large-scale pre-training.
Adopting foundation models together with traditional learning methods has
increasingly gained recent interest research community and shown potential for
real-world applications. In this paper, we present a comprehensive survey of
recent progress on annotation-efficient learning for medical image segmentation
utilizing weak annotations before and in the era of foundation models.
Furthermore, we analyze and discuss several challenges of existing approaches,
which we believe will provide valuable guidance for shaping the trajectory of
foundational models to further advance the field of medical image segmentation.
