A proactive dialogue system refers to a conversational system designed to guide the direction of a conversation in order to achieve pre-defined targets or fulfill specific goals. Recent studies have shown that Proactive Chain-of-Thought, which guides the system to explicitly think through intermediate reasoning and action-planning steps toward a conversational goal before generating a response, can significantly enhance the performance of proactive dialogue systems. However, these improvements primarily focus on prompt-based control, while the potential of fine-tuning Proactive-CoT remains largely unexplored. Furthermore, fine-tuning Proactive-CoT requires manual annotation of reasoning processes and action plans, which incurs significant time and cost. In this study, we propose a novel approach for automatically annotating reasoning processes and action plans through self-learning. This method enables fully automated annotation, significantly reducing the time and cost associated with manual annotation. Experimental results show that models trained using our proposed method outperform those trained with other fine-tuning approaches. These findings highlight the potential of self-learning approaches to advance the development of more robust and efficient proactive dialogue systems.