It is difficult for current object segmentation models to reach a good balance between segmentation performance and inference efficiency. To solve this challenge， a self-distillation object segmentation method via scale-attention knowledge transfer was proposed. Firstly， an object segmentation network only using features in backbone was constructed as the inference network， to achieve efficient forward inference process. Secondly， a self-distillation learning model via scale-attention knowledge was proposed. On the one hand， a scale-attention pyramid feature module was designed to adaptively capture context information at different semantic levels and extract more discriminative self-distillation knowledge. On the other hand， a distillation loss was constructed by fusing cross entropy， KL （Kullback-Leibler） divergence and L2 distance. It drove distillation knowledge to transfer into segmentation network efficiently to improve its generalization performance. The method was verified on five public object segmentation datasets of COD （Camouflaged Object Detection）， DUT-O （Dalian University of Technology-OMRON）， SOC （Salient Objects in Clutter）， etc.： considering the proposed inference network as the baseline network， the proposed self-distillation model can increase the segmentation performance by 3.01% on F_β metric， which was 1.00% higher better than that of Teacher-Free （TF） self-distillation model； compared with recent Residual learning Net （R2Net）， the proposed object segmentation network reduces the number of parameters by 2.33×106， improves the inference frame rate by 2.53%， decreases the floating-point operations by 40.50%， and increases segmentation performance by 0.51%. Experimental results show that the proposed self-distillation segmentation method can balance performance and efficiency， and is suitable for scenarios with limited computing and storage resources.