Accurate cancer segmentation in PET-CT images is crucial for oncology, yet remains challenging due to lesion diversity, data scarcity, and modality heterogeneity. Existing methods often struggle to effectively fuse cross-modal information and leverage self-supervised learning for improved representation. In this paper, we introduce C2MAOT, a Cross-modal Complementary Masked Autoencoder with Optimal Transport framework for PET-CT cancer segmentation. Our method employs a novel modality-complementary masking strategy during pre-training to explicitly encourage cross-modal learning between PET and CT encoders. Furthermore, we integrate an optimal transport loss to guide the alignment of feature distributions across modalities, facilitating robust multi-modal fusion. Experimental results on two datasets demonstrate that C2MAOT outperforms existing state-of-the-art methods, achieving significant improvements in segmentation accuracy across five cancer types. These results establish our proposed method as an effective approach for tumor segmentation in PET-CT imaging. Our code is available at https://github.com/hjj194/c2maot .