A model-free adaptive heading control strategy for unmanned surface vessels (USVs) is proposed by integrating extended state observer (ESO) with model-free adaptive control. To handle internal model perturbations and external disturbances, a discrete linear ESO is employed to estimate the aggregate uncertainties. Then, the model-free adaptive heading controller is constructed by utilizing system input and output data, and the estimation information deriving from the ESO. Furthermore, a dynamic event-triggered mechanism (ETM) is designed and embedded in the controller to save communication data. Compared with the periodic sampling, the introduction of the dynamic ETM enables the controller to achieve a comparable control performance with only 5.35% of the communication frequency. Simulation comparison and analysis exhibit that the proposed heading controller can achieve superior heading control effect for USVs and show broader application prospect.