I've been struggling to quantify and explain diversity for a while and I think I've found something which captures the essence of a portfolio manager's ability to diversify away risk.
Say you have a portfolio where each stock has volatility $\sigma_i$, weight $w_i$, and pairwise correlation $\rho_{ij}$. Then the portfolio's volatility $\sigma$ can be computed as:
$$\sigma^2 = \underbrace{\sum_i w_i^2 \sigma_i^2}_A + \underbrace{\sum_i\sum_{j \neq i}w_i w_j \sigma_i \sigma_j \rho_{ij}}_{B}$$
Part $A$ of the equation is a pure volatility part, and part $B$ is correlation-dependent part which portfolio managers are forever trying to minimise my choosing stocks which are not too correlated to each other.
Surprisingly, the $A$ part is usually much smaller than the $B$ part. For example, when I calculate the MSCI AC World's volatility in this way (using a five years of monthly returns of stocks currently in the index), I find $A = 0.01\%$ and $B = 1.04\%$. When I split my fund's portfolio risks in this manner, I always find $B$ to be much bigger than $A$ (four to ten times bigger).
Since the goal is to minimize $B$, I propose $\sigma^2/A$, or
$$\frac{\sigma^2}{\sum w_i^2 \sigma_i^2}$$
as a ratio for measuring diversity: the lower the better. It's similar to Goetzmann & Kumar's Normalized portfolio variance which @jaamor mentioned in his answer, but makes the denominator more relevant to the portfolio you're trying to measure (a lot of information is lost in a straight average $\bar{\sigma}$ of the $\sigma_i$'s).
Edit: I think it makes even more sense to scale the denominator somehow, so portfolios with low numbers of stocks don't get a misleadingly bad score (or high numbers of stocks good scores). Maybe divide by $\sum w_i^2$ or multiply by the number of stocks.