Estimating age from a single facial image has achieved promising results. However, accurately predicting age remains challenging due to the inherent complexity of facial features and significant individual variations. To address this, we propose a Parsing-induced Mixture-of-Experts (PMoE) framework to adaptively extract and utilize discriminative facial features. Our method consists of three components: the image encoder, the PMoE module, and the adaptive age decoder. The PMoE module leverages multiple expert modules to collaboratively process diverse facial features, capturing the local features critical for accurate age estimation. To achieve precise local feature extraction, we introduce a feature-focused face parsing operation to generate facial parsing feature maps that contain semantic information. Finally, the adaptive age decoder combines local and global features, enhancing age estimation performance. Extensive experiments on two benchmark datasets validate the effectiveness of our approach, demonstrating that the PMoE framework significantly outperforms existing methods in facial age estimation accuracy.