In this paper, we propose a Dual-Constraint Diffusion Model (DCDM) to contrast aging appearance for facial age estimation, addressing the key issue of noisy labels. Existing methods for face age estimation are plagued by class imbalance and noisy supervision signals, which disrupt the ordinal relationships between age categories and hinder effective feature decoupling in existing models. To overcome these challenges, the proposed DCDM develops a label-independent Paired Comparison, ensuring accurate sample labeling and maintaining continuity in age estimation. Moreover, we incorporate a Dual-Constraint Diffusion Model to effectively separate and recombine age-related and unrelated features, thus facilitating the generation of high-fidelity and continuous age-progressed facial representations. Lastly, we optimize our model parameters by exploiting the age difference information via an active learning framework. Comparative evaluations on several in-the-wild datasets demonstrate that our DCDM significantly achieves superior results compared to existing state-of-the-art methods in facial age estimation.