"If a model has poor discrimination, no adjustment or calibration can correct the model. If discrimination is good, the predictor can be calibrated without sacrificing discrimination. Here, calibrating predictions means modifying them without changing their rank order"
Tutorial in biostatistics: Multi variable prognostic models : issue in developing models, evaluating assumptions and adequacy and measure and reducing errors. Harrell et al. 1996).
So, I would be inclined to say that poor discrimination, but good calibration is a problem. But this is also a strange result. If you have a high probability, such a 0.9, to be perfectly calibrated you would need to have an observed frequency of 90% for the case (for example, in this decile 90% of patients do have cancer), and this relationship needs to be true throughout. In order to achieve something like that, I am inclined to say you need good discrimination too (i.e. False positives may bring down the observed frequency at the high end and vice versa, thus resulting in poor calibration).
Maybe you need to make sure that your calibration is actually "good" and discrimination "bad".