<p>Recent advances in medical image segmentation using deep learning have developed dental technology. However, many dental clinics lack the minimum hardware infrastructure required to run high-performance deep learning algorithms in real-time, hindering their commercialization. Although filter pruning and knowledge distillation hold promise as resource-efficient solutions, they remain underexplored in medical/dental image segmentation. This paper presents a novel framework for compressing dental image segmentation networks. First, we efficiently explored sub-networks by removing unnecessary filters through a learnable differentiable gate. Second, during sub-network exploration, we further utilized the baseline network’s information through masked knowledge distillation. Through this approach, the proposed compression framework efficiently explores a more appropriate sub-network with minimal loss of the baseline network’s information for each structure. As a result, the proposed method was tested on dental anatomical structure segmentation on 3D CBCT and teeth segmentation on panoramic radiographs, achieving reductions in MACs by 90% and 95%, respectively, while showing only around a 1% decrease in performance based on the Dice score. The ability to achieve up to 95% MAC reduction with minimal Dice degradation highlights its potential for real-time deployment in resource-limited dental clinics, paving the way for practical clinical adoption.</p>