User intention estimation is essential in shared control systems for powered wheelchairs. It enables seamless navigation assistance that enhances safety, efficiency, and usability, while preserving user autonomy and reducing effort. This paper presents Diffusion-based Wheelchair User Intention Estimation (DIWIE), a novel multimodal learning framework that leverages a Denoising Diffusion Probabilistic Model (DDPM) to forecast multiple plausible future trajectories, addressing uncertainty in human behaviour. DIWIE conditions on diverse inputs, including obstacle information, user attention cues from eye gaze and head pose, semantic context, wheelchair kinematics, and joystick commands, operating without predefined maps or target destinations. Evaluated on a large new dataset of natural navigation by multiple drivers, DIWIE outperforms state-of-the-art methods, achieving lower displacement errors and collision rates, making it a valuable component for integration into shared control systems. This work also analyses the relevance of different data sources for intention estimation and aligns evaluation metrics with related fields to foster reproducibility.