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In sequence-to-sequence Transformer-based automatic speech recognition (ASR), autoregressive (AR) models achieve strong accuracy but suffer from slow decoding, while non-autoregressive (NAR) models enable parallel decoding at the cost of degraded performance. We propose a principled NAR ASR framework based on Masked Diffusion Models to significantly reduce this gap. A pre-trained speech encoder is coupled with a Transformer diffusion decoder conditioned on acoustic features and partially masked transcripts for parallel token prediction. To mitigate the training-inference mismatch, we introduce Iterative Self-Correction Training that exposes the model to its own intermediate predictions. We also propose a Position-Biased Entropy-Bounded Confidence-based sampler to further boost results. Experiments across multiple benchmarks demonstrate consistent gains over prior NAR models and competitive performance with strong AR baselines, while retaining parallel decoding efficiency.