Self-training with data augmentation emerges as an efficacious strategy for harnessing unlabeled data in the realm of semi-supervised medical image segmentation. Within the synthetic domain, existing models make a deliberate trade-off, sacrificing some of its absolute performance on labeled data to bolster its generalization capabilities on the predominantly abundant unlabeled data encompassed within the entire dataset. In this study, we find out the essence of employing data augmentation techniques to create a proxy data domain that serves as a bridge between labeled and unlabeled data. To this end, we optimize the aforementioned approach by incorporating the concept of curriculum learning, which encompasses two primary components: Dynamic Copy-Paste strategies and the Self-Iterative Segmentation Model. Concerning the former, the dynamic scaling of the copy-paste box guides the model in acquiring shared semantics, progressing from easier (labeled data) to more challenging (unlabeled data). In order to facilitate this incremental learning process, we have devised models that supports progressive iterative evolution throughout the training phase. Our approach has demonstrated remarkable efficacy through a comprehensive series of benchmarks, consistently outperforming existing methods and achieving state-of-the-art performance.