  Despite the remarkable performance of deep learning methods on various tasks,
most cutting-edge models rely heavily on large-scale annotated training
examples, which are often unavailable for clinical and health care tasks. The
labeling costs for medical images are very high, especially in medical image
segmentation, which typically requires intensive pixel/voxel-wise labeling.
Therefore, the strong capability of learning and generalizing from limited
supervision, including a limited amount of annotations, sparse annotations, and
inaccurate annotations, is crucial for the successful application of deep
learning models in medical image segmentation. However, due to its intrinsic
difficulty, segmentation with limited supervision is challenging and specific
model design and/or learning strategies are needed. In this paper, we provide a
systematic and up-to-date review of the solutions above, with summaries and
comments about the methodologies. We also highlight several problems in this
field, discussed future directions observing further investigations.
