Deformable Image Registration (DIR) is crucial for various medical image analysis tasks. However, deep learning methods struggle to balance registration accuracy and computational complexity. Incorporating knowledge distillation into registration emerges as a promising approach; however, these methods often rely on pre-designed or heuristically chosen teacher networks. Their efficiency is not optimal, primarily because they fail to account for gradient conflicts between student and teacher networks. In this paper, we propose a novel self-distillation paradigm with gradient surgery for end-to-end deformable image registration, named GSSD. Specifically, to design a universally applicable knowledge distillation paradigm, the teacher network is directly cloned from the student network and is removed after training, reducing hardware requirements upon deployment. To resolve potential gradient conflicts between the student and teacher networks, we introduce a two-stage gradient surgery optimization strategy by projecting the conflicting gradient into the normal plane of the dominant gradient, ensuring the distillation efficacy. Extensive experiments conducted on three publicly available datasets demonstrate consistent improvements over various methods, with no increase in inference time and parameters, especially more than 3% increase in Dice score for liver CT.