Diffusion Language Models (DLMs) enable parallel text generation by denoising multiple masked positions in a single pass, but confidence-threshold decoding often commits only the safest predictions, leaving informative uncertainty underused. We propose Plan-Verify-Fill (PVF), a training-free draft-and-verification decoding strategy that preserves the high-confidence decoding backbone while selectively testing lower-confidence candidates with high expected verified utility. PVF uses a calibrated planning-token vocabulary to allocate its limited verification budget, accepts proposed drafts only when they remain consistent with the backbone trajectory, and otherwise defaults to conservative confidence-gated unmasking. Across mathematical reasoning, knowledge, and code-generation benchmarks on LLaDA-8B-Instruct and Dream-7B-Instruct, PVF maintains comparable accuracy while delivering up to 2.3× higher throughput than baseline methods.