Autonomous drone racing has long struggled to match human-level performance in multi-agent competitive scenarios because single-agent policies implicitly assume a static world. This paper presents a multi-agent reinforcement learning (MARL) framework that trains racing drones to anticipate opponent trajectories and proactively choose safe, aggressive racing lines. The key contribution is a safety-conditioned action space in which each drone predicts the probability of collisions with nearby agents and selects actions that maximize race progress while keeping collision probability below a tunable threshold. The policy is trained entirely in simulation on a photorealistic racing track using centralized training with decentralized execution (CTDE), allowing agents to share experiences during training but act independently at test time. Remarkably, the policy transfers directly to real Crazyflie nano-drones without any real-world fine-tuning. In extensive outdoor experiments, the drones achieve lap times faster than the human world champion while maintaining zero mid-race collisions. This is the first demonstration of superhuman, safe, multi-agent drone racing and validates the hypothesis that MARL, when combined with careful safety conditioning, can produce policies that are both faster and safer than human experts.