Deformable image registration is a crucial task in medical image analysis, and its complexity has spurred significant research and ongoing progress. Much of the work in this area has concentrated on achieving incremental performance gains by adjusting network architectures or introducing new loss functions. However, these modifications are often tailored to specific tasks or datasets, which limits their general applicability. To address this limitation, we propose an innovative solution: a plug-in curriculum scheduler that can be seamlessly integrated into existing methods without changing their core architecture. Our scheduler, inspired by curriculum learning, progressively increases task difficulty to enhance performance, incorporating sample difficulty and matching accuracy as key criteria. Sample difficulty is assessed at voxel and volume levels, using Variance of Gradients (VoG) for voxel complexity and Gaussian blurring for volume evaluation, while matching accuracy involves gradually increasing supervision for improved alignment and accuracy. We empirically demonstrate that this scheduler achieves superior accuracy and visual quality in various tasks and datasets. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JYeShin/Plug-InCurriculumScheduler</uri>.