Model reuse aims at reducing the need of learning resources for a newly target task. In previous model reuse studies, the target task usually receives labeled data passively, which results in a slow performance improvement. However, learning models for target tasks are often required to achieve good enough performance rapidly for practical usage. In this paper, we propose the AcMR (Active Model Reuse) method for the rapid performance improvement problem. Firstly, we construct queries through pre-trained models to facilitate the active learner when labeled examples are insufficient in the target task. Secondly, we consider that pre-trained models are able to filter out not-very-necessary queries so that AcMR can save considerable queries compared with direct active learning. Theoretical analysis verifies that AcMR requires fewer queries than direct active learning. Experimental results validate the effectiveness of AcMR.