Multi-modal Entity Alignment (MMEA) aims to identify equivalent entities across diverse knowledge graphs by leveraging structural, attribute, and visual information. However, real-world datasets frequently suffer from missing modalities, necessitating feature imputation. A critical yet underexplored issue is that not all imputed modalities are inherently trustworthy. Ignoring the aleatoric uncertainty of such modalities introduces severe noise which propagates through the fusion process and degrades alignment performance. To address this challenge, we propose a novel MMEA framework, namely SURE, to Suppress Uncertainty for tRustworthy Entity alignment. Specifically, SURE introduces an uncertainty-aware variational imputation module to estimate the aleatoric uncertainty of generated features. Crucially, rather than using these imputation features blindly, SURE leverages the estimated uncertainty to suppress noise propagation via a confidence-gated multi-modal fusion and an adaptive contrastive learning objective. Extensive experiments on DBP15K datasets demonstrate that SURE significantly outperforms state-of-the-art baselines, exhibiting exceptional robustness particularly in scenarios with high modality missing rates.