Personalized news headline generation has emerged as a promising avenue for optimizing user engagement and satisfaction in online news consumption. However, prevailing methods predominantly adopt an indirect modeling paradigm, inferring user preferences from related tasks rather than capturing their direct headline preferences. Direct modeling remains underexplored due to two primary challenges: (1) the scarcity of large-scale personalized headline datasets and (2) the risk of factual inaccuracies when models overly cater to subjective user preferences. To address these challenges, we propose USG, a novel multi-agent user simulator headline generation framework. USG introduces three specialized agents: the User provides subjective feedback to guide headline personalization; the Editor enforces editorial standards (i.e., factual consistency and no clickbait); and the Writer iteratively refines candidates via a Critique-and-Refine algorithm guided by feedback from the User and Editor agents. The multi-agent interactions yield optimal personalized headlines and reasoning chains distilled from the interaction trajectories, forming a high-quality dataset. This dataset directly supervises the fine-tuning of a standalone generator. Extensive experiments demonstrate that USG outperforms the strongest baseline by nearly 6% on personalization, achieving state-of-the-art performance on both personalization and factual consistency, while enabling efficient end-to-end inference. Our code is available at https://github.com/Leah-HKBU/USG.