Interactive medical segmentation based on human-in-the-loop is a novel paradigm that draws on human expert knowledge to assist medical image segmentation. However, existing methods often fall into what we call the \textit{interactive misunderstanding}, the essence of which is the dilemma in trade-off \textit{short-} and \textit{long-term} interaction information. To better utilize the interactive information at various timescales, we propose an interactive segmentation framework, called interactive {\bf{ME}}dical segmentation with self-adaptive {\bf{C}}onfidence {\bf{CA}}libration ({\bf{MECCA}}), which combines the action-based confidence learning and multi-agent reinforcement learning. A novel confidence network is learned by predicting the alignment level of the action with the short-term interactive information. A confidence-based reward shaping mechanism is then proposed to explicitly incorporate the confidence into the policy gradient calculation, thus directly correcting the model's interactive misunderstanding. Furthermore, MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance, respectively. Numerical experiments on different segmentation tasks show that MECCA can significantly improve short- and long-term interactive information utilization efficiency with remarkably fewer labeled samples. The demo video is available at \url{https://bit.ly/mecca-demo-video}.