  Medical image segmentation is one of the fundamental problems for artificial
intelligence-based clinical decision systems. Current automatic medical image
segmentation methods are often failed to meet clinical requirements. As such, a
series of interactive segmentation algorithms are proposed to utilize expert
correction information. However, existing methods suffer from some segmentation
refining failure problems after long-term interactions and some cost problems
from expert annotation, which hinder clinical applications. This paper proposes
an interactive segmentation framework, called interactive MEdical segmentation
with self-adaptive Confidence CAlibration (MECCA), by introducing the
corrective action evaluation, which combines the action-based confidence
learning and multi-agent reinforcement learning (MARL). The evaluation is
established through a novel action-based confidence network, and the corrective
actions are obtained from MARL. Based on the confidential information, a
self-adaptive reward function is designed to provide more detailed feedback,
and a simulated label generation mechanism is proposed on unsupervised data to
reduce over-reliance on labeled data. Experimental results on various medical
image datasets have shown the significant performance of the proposed
algorithm.
