  Active learning generally involves querying the most representative samples
for human labeling, which has been widely studied in many fields such as image
classification and object detection. However, its potential has not been
explored in the more complex instance segmentation task that usually has
relatively higher annotation cost. In this paper, we propose a novel and
principled semi-supervised active learning framework for instance segmentation.
Specifically, we present an uncertainty sampling strategy named Triplet Scoring
Predictions (TSP) to explicitly incorporate samples ranking clues from classes,
bounding boxes and masks. Moreover, we devise a progressive pseudo labeling
regime using the above TSP in semi-supervised manner, it can leverage both the
labeled and unlabeled data to minimize labeling effort while maximize
performance of instance segmentation. Results on medical images datasets
demonstrate that the proposed method results in the embodiment of knowledge
from available data in a meaningful way. The extensive quantitatively and
qualitatively experiments show that, our method can yield the best-performing
model with notable less annotation costs, compared with state-of-the-arts.
