Medical benchmarks are dominated by single-turn, multiple-choice clinical cases that poorly reflect real consultations. Practically, clinicians elicit evidence interactively, and patient communication varies widely. We introduce MEDROUNDSQA, a multi-turn diagnostic benchmark derived from 1,387 clinical cases across 17 specialties. Each case is converted into a structured 24-slot clinical record and then instantiated as controlled doctor–patient dual-agent dialogues under varying patient personas, with the underlying clinical content held fixed. We further classify cases by difficulty using model-based uncertainty to enable easy-to-hard analysis. Evaluations of fifteen LLM doctor agents show that (i) moving from a single-turn diagnosis on the standardized records to multi-turn consultations causes large degradations of roughly 13–39 points; (ii) more turns reliably improve question relevance, but diagnostic accuracy exhibits diminishing returns and typically plateaus after 6–12 turns; and (iii) patient persona differences can shift diagnosis accuracy by about 7–8 points from the lowest to highest education levels, highlighting equity risks that single-turn benchmarks miss.