Test-time scaling is a family of techniques to improve LLM outputs at inference time by performing extra computation. To the best of our knowledge, test-time scaling has been limited to domains with verifiably correct answers, like mathematics and coding. We transfer test-time scaling to the LeWiDi-2025 tasks to evaluate annotation disagreements. We experiment with three test-time scaling methods: two benchmark algorithms (Model Averaging and Majority Voting), and a Best-of-N (BoN) sampling method. The two benchmark methods improve LLM performance consistently on the LeWiDi tasks, but the BoN method does not. Our experiments suggest that the BoN method does not currently transfer from mathematics to LeWiDi tasks, and we analyze potential reasons for this gap.