We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as an adaptive (online) adversary—one who sees all activity of the forecaster except the randomization that the forecaster may deploy. A number of papers have proposed randomized forecasting strategies that achieve a calibration error rate of O(1/√(T)), which we prove is tight in general. On the other hand, it is well known that it is not possible to be calibrated without randomization, or if nature also sees the forecaster's randomization; in both cases the calibration error could be Ω(1). Inspired by the equally seminal works on the power of two choices (Azar et al., 1994) and imprecise probability theory (Walley and Fine, 1982), we study a small variant of the standard online calibration problem. The adversary gives the forecaster the option of making two (nearby) probabilistic predictions, or equivalently an interval forecast of small width, and the endpoint closest to the revealed outcome is used to judge calibration. This power of two choices (or imprecise forecast) accords the forecaster with significant power—we show that a faster calibration rate of O(1/T) can be achieved even without deploying any randomization.